DeepPlanner4Cardio: an automatic multi-view planning tool for CMR

Motivation: Manual planning of the 4 standard cardiac views (2-chamber, 3-chamber, 4-chamber, short axis) is currently a time-consuming process that demands significant operator interaction and expertise. Goal(s): Our aim is to streamline and automate this process to reduce examination times and minimize dependency on highly trained operators. Approach: We developed DeepPlanner4Cardio, a deep learning model designed to predict the standard cardiac views simultaneously. Results: DeepPlanner4Cardio achieved a clinically acceptable accuracy of 94% on the test set with a mean orientation error of 11º and a mean displacement error of 5.9 mm, both within the inter-operator variability ranges. Impact: DeepPlanner4Cardio effectively supports CMR operators by providing an accessible, automated solution to enable fast and reproducible cardiac view planning, demonstrating great potential to be applied in a clinical setting.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1966
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

DeepPlanner4Cardio: an automatic multi-view planning tool for CMR

Markus Henningsson, Pedro Osório, Rita Nunes, Teresa Correia et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Manufacturing Process and Optimization
article

DeepPlanner4Cardio: an automatic multi-view planning tool for CMR

Markus Henningsson, Pedro Osório, Rita Nunes, Teresa Correia, João de Deus Gomes dos Santos
article en

Abstract

Motivation: Manual planning of the 4 standard cardiac views (2-chamber, 3-chamber, 4-chamber, short axis) is currently a time-consuming process that demands significant operator interaction and expertise. Goal(s): Our aim is to streamline and automate this process to reduce examination times and minimize dependency on highly trained operators. Approach: We developed DeepPlanner4Cardio, a deep learning model designed to predict the standard cardiac views simultaneously. Results: DeepPlanner4Cardio achieved a clinically acceptable accuracy of 94% on the test set with a mean orientation error of 11º and a mean displacement error of 5.9 mm, both within the inter-operator variability ranges. Impact: DeepPlanner4Cardio effectively supports CMR operators by providing an accessible, automated solution to enable fast and reproducible cardiac view planning, demonstrating great potential to be applied in a clinical setting.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Manufacturing Process and Optimization
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